Method and apparatus for acquiring overlapped medical image slices

ABSTRACT

The disclosure describes a technique for medical imaging, referred to herein as the Rapid Interleave Overlap Technique (RIOT), wherein image data is acquired as a plurality of series sequences in a manner that allows for unlimited overlap. RIOT involves interleaving and overlapping 2D image slices of multiple series of image data of the same ROI into a composite data set from which MPR and 3D reconstructions exhibiting excellent resolution properties and crisp image quality can be generated.

CROSS REFERENCE AND PRIORITY CLAIM TO RELATED APPLICATIONS

This application is a continuation of patent application Ser. No.11/211,397, filed Aug. 25, 2005, entitled “Method and Apparatus forAcquiring Overlapped Medical Image Slices, now U.S. Pat. No. 8,046,044,which claims priority to provisional patent application Ser. No.60/604,214 filed Aug. 25, 2004, entitled “Method and Apparatus forAcquiring Overlapped Medical Image Slices” and 60/679,561, filed May 10,2005, entitled “Method and Apparatus for Acquiring Overlapped MedicalImage Slices”, the entire disclosures of each of which are incorporatedherein by reference.

FIELD OF THE INVENTION

The present invention relates to medical imaging, in particular magneticresonance (MR) imaging and computed tomography (CT) imaging.

BACKGROUND OF THE INVENTION

Multi-planar (MPR) and three-dimensional (3D) reconstructions oftwo-dimensional (2D) image slice data can be very helpful to clinicians,particularly in connection with surgical planning. However, current MRimaging techniques for acquiring medical image slices of a patient'sregion of interest (ROI) are of limited value when high quality MPR and3D reconstructions are desired. In MR, the slice thickness for a givenacquisition is constrained by multiple factors such as sequence,specific absorption rate (SAR) and field of view (FOV). In addition, inMR there is only one “detector,” the coil unit (e.g., the head coil).Since there is only one detector with MR imaging (unlike CT imaging,wherein multi-detector CT scanners can use several detectors tosimultaneously acquire image data), acquisition parameters such as baseresolution and slice thickness have much more of a direct impact onacquired images. This limits the ability to change parameters readilybefore and after image acquisition. In MR there is always a trade-off.For example, although a smaller slice thickness will yield betterresolution, smaller slice thickness will also result in decreased signalto noise (SNR), thereby leading to increased scan time and SAR. Eachsequence therefore has an allowable slice thickness range. In addition,as the slice thickness decreases, other factors such as “cross-talk” mayincrease, thereby further reducing image quality. This in turn limitsthe ability to overlap slice information, a problem that has plaguedconventional high resolution MR MPR and 3D reconstruction techniques.

In order to produce high quality MPR and 3D images, an overlap of 50% isdesirable. For the reasons stated above, traditional MR techniques donot allow for this degree of overlap of 2D images. Currently, the bestMR techniques available for generating 3D images are believed to bevolumetric acquisitions, where the base image resolution can be quitegood. However, as stated above, with the increase in resolution for suchtechniques, the SNR is decreased, with the end result being that eventhe most optimal 3D MR sequence cannot compare to the 3D images derivedby current multi-detector CT technology. 3D reconstructions derived from2D MR acquisitions are even more limited. These images, which havelimited resolution and poor SNR, do not produce 3D volumetric imageswith the quality required for many diagnostic assessments.

Additional background information pertaining to MR imaging issues can befound in the following publications, the entire disclosures of each ofwhich are incorporated herein by reference: Sailhan F, Chotel F, GuibalA L, Gollogly S, Adam P, Berard J, Guibaud L: Three-dimensional MRimaging in the assessment of physeal growth arrest, European Radiology,2004; Apr. 3 [Epub ahead of print]; Klingebiel R, Thieme N, Kivelitz D,Enzweiler C, Werbs M, Lehmann R: Three-dimensional imaging of the innerear by volume-rendered reconstructions of magnetic resonance data,Archives of Otolaryngology Head and Neck Surgery, 2002; 128:549-53; LeeV S, Lavelle M T, Krinsky G A, Rofsky N M: Volumetric MR imaging of theliver and applications, Magnetic Resonance Imaging Clinics of NorthAmerica, 2001; 9:697-716; Kleinheinz J, Stamm T, Meier N, Wiesmann H P,Ehmer U, Joos U: Three-dimensional magnetic resonance imaging of theorbit in craniofacial malformations and trauma, International Journal ofOrthodontic Orthognath Surgery 2000; 15:64-8.; Krombach G A,Schmitz-Rode T, Tacke J, Glowinski A, Nolte-Ernsting C C, Gunther R W:MRI of the inner ear: comparison of axial T2-weighted, three-dimensionalturbo spin-echo images, maximum-intensity projections, and volumerendering, Investigational Radiology, 2000; 35:337-42; McKinnon G C,Eichenberger A C, von Weymarn C A, von Schulthess G K; Ultrafast imagingusing an interleaved gradient echo planar sequence in Books ofAbstracts, 11^(th) Annual Meeting, Society of Magnetic Resonance inMedicine, 1992; 106; Phillips M D, Lowe M J, Lurito J T, Dzemidzic M,Mathews V P: Temporal lobe activation demonstrates sex-based differencesduring passive listening; Radiology, 2001; 220:202-07; and Haacke E. M.,Brown R. W., Thompson M. R. and Venkatesan R.: Magnetic ResonanceImaging Physical Principles and Sequence Design, John Wiley & Sons,1999.

SUMMARY OF THE INVENTION

A goal of the present invention is to provide a solution to theshortcomings discussed above. In accordance with one aspect of thepreferred embodiment of the present invention, image data is acquired asa plurality of series sequences in a manner that allows for unlimitedoverlap. This technique, which will be described in greater detailbelow, is referred to herein as the Rapid Interleave Overlap Technique(RIOT).

The RIOT technique involves interleaving and overlapping 2D image slicesof multiple series of image data of the same ROI into a composite dataset from which MPR and 3D reconstructions exhibiting excellentresolution properties and crisp image quality can be generated. It isalso believed that the image slices within the composite data set can besegmented into thinner slices. Starting from an initial position in ascanner's coordinate system (preferably an initial table position), theRIOT technique comprises acquiring an image data series, wherein theimage data series comprises a plurality of image slices having aspecified slice thickness and a specified skip therebetween. After thisseries is acquired, the initial table position is reset to new valueoffset by an amount determined as a function of the desired degree ofoverlap for the resultant aggregated image. Next, starting from this newinitial table position, another image data series is acquired with thesame slice thickness and skip parameters as the previous series.Thereafter, depending upon the desired degree of overlap, the initialtable position is adjusted again and yet another image data series withthe same slice thickness and skip parameters as the previous series isacquired, albeit starting from the new initial table position. If anoverlap of 50% is desired, four series will preferably be acquired. Ifan overlap of 100%, eight series will preferably be acquired.

After all of the image data series have been acquired, the image slicesof these series can be assembled into a composite data set. The imageslices within the composite data set are preferably sorted by sliceposition (table position) and re-ordered within the composite data setby ascending or descending slice position (table position). Alsodisclosed herein is an automated software program configured to performthis sorting operation.

With RIOT, “cross-talk” between image slices can be virtually eliminatedso that excellent SNR can be maintained even though images are segmentedat effectively smaller slice thicknesses than the original image slicesof the original data series. Furthermore, multiple series can beinterleaved with as large of an overlap as is needed, even as high as100%. In other words, RIOT allows for essentially an unlimited amount ofuser-configurable overlap, a feature not believed to be possible evenwith current CT technology. Experimentation with RIOT indicates that anoverlap of 50% will yield excellent MPRs and 3D reconstructions withenhanced image detail.

Also, in addition to the option of the acquiring image slices in thestandard axial, sagittal, and a coronal planes, it is preferred that thepresent invention also be configured to allow for the acquisition ofimage slices in planes of any obliquity.

These and other features and advantages of the present invention are setforth below and in the enclosed figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an overview of a preferred image data acquisition systemin which the present invention is implemented;

FIG. 2 is a flowchart illustrating a preferred implementation of thepresent invention;

FIGS. 3(a)-(d) illustrate an exemplary application of the presentinvention wherein 4 series of image slices are acquired to obtain a 50%overlap;

FIGS. 4(a)-(h) illustrate an exemplary application of the presentinvention wherein 8 series of 4 mm image slices are acquired to obtain a100% overlap;

FIGS. 5(a)-(d) illustrate various 3D reconstructions of phantom images;

FIG. 6 illustrates a 3D image derived from a high resolution 3D sequence(3D Vibe);

FIGS. 7(a) and (b) illustrate 3D and MPR's of the lower extremities of apatient applying RIOT to True-FISP sequence with 100% overlap;

FIGS. 8(a)-(k) illustrate a sequence of screenshots illustrating how aseries of image slices can be acquired in any obliquity;

FIG. 9 depicts an exemplary preferred algorithm for sorting image slicesby slice position;

FIGS. 10(a) and (b) depict curve p(z); and

FIG. 11 depicts slices of thickness Δ.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 1 illustrates a preferred image data acquisition system 100 inaccordance with the teachings of the present invention. An MR scanner102 is used to acquire 2D image data corresponding to a patient's ROIalong selected ones of the xy (coronal), xz (sagittal), and yz (axial)planes. If desired, 2D image data can also be obtained in planes ofother specified obliquities. The scanner 102 acquires 2D image slices inaccordance with instructions provided by a clinician via a scannerinterface control computer 104. Through control computer 104, aclinician can specify the necessary slice parameters for a givenacquisition, as is readily understood in the art. The resultant imagedata acquired by the scanner 102 are then returned to control computer104 for further processing thereby. A preferred system 100 suitable foruse with the present invention is a 1.5 Tesla Siemens Sonata MagnetomSystem manufactured by Siemens Medical Systems of Erlangen, Germany. Itshould be understood, however, that other image data acquisition systemscan be used in the practice of the present invention. Furthermore, thetechnique of the present invention is believed to be suitable for usewith any type of coil unit.

The present invention arises in the manner by which the scanner acquiresimage data. FIG. 2 is a flowchart illustrating this process, which canbe applied to any 2D sequence. With RIOT, the slice acquisitionparameters for each acquired series are the same except for tableposition (TP). The field of view (FOV) can be set at 100%, but phaseover sampling may be used so long as it is identical for all seriessequences. Fat saturation may also optionally be used. Furthermore, theaverage (NEX) can be set to one (or can be multiple so long as eachimage data series has the same number of averages). The skip betweenimage slices is set to 100%. It is also strongly preferred that theplane of acquisition be the same as the desired plane of themulti-planar or 3D reconstruction, as the resolution is believed to bebest in the plane of acquisition. For instance, if the desired 3Drepresentation or MPR is in the coronal plane, then the series should beacquired coronally. Earlier experiments had suggested that in order toachieve adequate interleaving the plane of each series sequence had tobe defined relative to the table and not the ROI being scanned. Forexample, these earlier experiments suggested that the coronal planeshould be defined off of an axial image by lines running parallel to the“X” axis of the axial localizer. In other words images were to beacquired in the axial, sagittal or coronal plane relative to the tableitself, not the anatomy being imaged. However, later experiments havedetermined that the plane of acquisition can be prescribed in anyobliquity.

Experimentation also indicates that an original slice thickness of 4 mmis optimal for reasons that will discussed below. 4 mm is the preferredoriginal slice thickness because an original slice thickness below 4 mmwould complicate the mathematical calculations without significantchange in scan times. Also, because the ultimate slice thickness isprimarily the result of the number of series that are interleaved, athinner original slice thickness will not have a significant effect interms of the thickness of the resultant composite series. For example,If 4 series of 4 mm thick slices are interleaved and overlapped by 50%,the resultant composite series will have a 2 mm slice thickness. If 4series of 3 mm slices are interleaved and overlapped by 50%, theresultant composite series will have a 1.5 mm slice thickness.

Nevertheless, if necessary, slice thicknesses thinner or thicker than 4mm can be used and are also suitable for the practice of the presentinvention.

With reference to FIG. 2, at step 200, the scanner 102 acquires a firstimage data series starting from table position TP(0). It is worth notingthat it is believed to be best to start with the TP dictated by thescanner rounded off to the nearest whole number, in which case TP(0) canbe thought of as whatever the starting table position dictated by thescanner is. However, other starting table positions may optionally beused. Furthermore, experimentation shows that setting the TP of thefirst series at true 0 or close to true 0 can result in errors. However,it is believed that this can be remedied by an automated softwareprogram as described below. FIG. 3(a) illustrates an example of theresult of this step, wherein four image slices of thickness n mm areacquired starting at TP(0 mm) with a 100% skip therebetween (i.e., askip of n mm). While the example of FIGS. 3(a)-(d) depict theacquisition of four slices per series, it should be understood that moreor fewer slices can be acquired each sequence.

A skip of 100% is strongly preferred because a skip of less than 100%would require that each series have a different skip in order to achievea desired overlap. For instance, theoretically, a 50% overlap can alsobe achieved by interleaving 3 series instead of 4. In this setting,Series A (with a slice thickness of 4 mm, TP (0) and skip of 100%) wouldhave to be overlapped with series B (slice thickness, 4 mm, TP (4) andskip of 100%. This is possible and would yield a composite series with 4mm slices and 0 skip. However, in order to achieve a 50% skip with only3 series, the third series would have to have a TP of (2) and a skip of50%. Accordingly, to accommodate a skip not equal to 100%, specialsoftware would have to be designed to accommodate interleaving of theslices. In addition, another problem arising from the use of a skip lessthan 100% is the “cross-talk” which may result, thereby causing adegradation of signal to noise ratio.

After the first series is acquired, the starting table position for thenext series is then adjusted (step 202). Starting table positionadjustments between series will be based on fractions of the originalslice thickness as determined by a desired degree of overlap. Thestarting table position of the next series is preferably adjusted by n/kmm, wherein the value of k depends upon the desired degree of overlap.If a 50% overlap is desired, k is preferably 2. If a 100% overlap isdesired, k is preferably 4. A slice thickness of 4 mm, facilitates thesecalculations as it will result in TP adjustments in whole numbers ratherthan in fractional increments. However, as noted above and below, whereautomated software is utilized to perform table position adjustments,this concern is attenuated.

While the examples of FIGS. 3(a)-(d) and 4(a)-(h) depict the startingtable position being incremented by n/k mm for each series, it should bereadily understood that the starting table position can also bedecremented by n/k mm for each series. Again, experimentation with theSiemens scanner shows that decrementing will not always work if thestarting TP of the first series is set at true 0 or slightly greater orless than true 0. The reason for this is that in this setting some ofthe series will require a starting TP of a positive value, while otherswill require a starting TP which is of a negative value. This willresult in errors with interleaving by the control computer. Again, it ispossible, however, that this problem can be remedied via a dedicatedsoftware program.

Table position (TP) shifts can be adjusted in any obliquity. However,with the preferred RIOT technique, all TP adjustments must be made inthe same direction for a given set of acquisitions (either positive ornegative), defined by the plane of the acquisition. As stated above,experiments show that the resolution of the 3D or MPR reconstruction isoptimal when the plane of the acquisition is in the desired plane of the3D or MPR reconstruction.

As stated above, the preferred embodiment can optionally be configuredto acquire images in planes of any obliquity, which is believed tooptimize visualization of the anatomy being scanned. This feature canpreferably be achieved under user or software control by a parameterchange in the chosen image acquisition protocol, which is believed to bepossible with any conventional 2D sequence. Upon opening the sequence,the number of slices, the slice thickness and the orientation (i.e.obliquity) of the slices is assigned, as with any 2D sequence, off of alocalizer image obtained in the axial, sagittal or coronal plane. Thetrajectory of the slices is then assigned by simply rotating, from aconsole for the imaging system using the available input commands to thesystem, the reference lines (indicating the trajectory of the slices) toa desired obliquity by an appropriate user input (e.g., a left mouseclick and hold) (see FIG. 8(a)). It should be noted that theinstructions provided herein are as per the preferred Siemens scanner,but the methodology described therefor can be readily applied to otherscanners. Next, the “position” parameter under the exam card of thesequence is selected and the position “mode” selected to be“offcenter-shift.” (see FIG. 8(b)). FIG. 8(b) shows the obliquity as atransverse image, but the line 800 defining the plane of acquisition canbe dragged and changed to any orientation by appropriate user orsoftware action (such as left mouse clicking on line 800 andmanipulating line 800 to the desired obliquity). Once line 800 ispositioned as desired, subsequent series will be oriented according tothe plane defined line 800 with the shifted positions applied to thatprescribed oblique plane. The selection of “offcenter-shift” as shown inFIG. 8(b) is in contrast to the only other option under position mode,i.e. “L-P-H” (which stands for Left, Posterior and Head, correspondingto the coronal, sagittal and axial planes respectively, relative to thetable) as originally described. The “offcenter-shift” mode instructs thescanner to orient subsequent position shifts in the obliquity of theprescribed slices rather than in the coronal, sagittal, and axial planerelative to the table. After the first acquisition is complete (see FIG.8(c)), subsequent acquisitions are prescribed by changing the positionshift parameter only by 1 mm or 2 mm depending on the desired netoverlap (that is, 1 mm for 100% and 2 mm for 50% (where slice thicknessn equals 4 mm)), as shown in FIGS. 8(d)-(f), which depict 2 mmincrements. This will yield 4 separate series for 50% overlap (see FIG.8(g)) and 8 separate series for 100% overlap. The series can then becombined and interleaved accordingly by selecting (e.g., by a left mouseclick) each series icon (representing each series) while pressing thecontrol key, selecting “select series” under the “edit” menu (see FIG.8(h)) and selecting “save as” under file (see FIG. 8(i)). The totalnumber of images of this composite series will appear. The user shouldmake sure that the composite series includes the correct number ofimages i.e. four times that of each individual series (for a four seriesoverlap) and eight times that of each individual series (for an eightseries overlap). In the example of FIGS. 3(a)-(d), the composite datafile will be made up of 4*4 slices (i.e., 4 series of 4 slices each, or16 slices). In the example of FIGS. 4(a)-(g), the composite data filewill be made up of 8*x (i.e., 8 series of x slices each). The compositeseries is then named (see FIG. 8(j)). Before saving the named series, itis important to ensure that the preset sorting under the patient file isset at “slice position” (see FIG. 8(k) as indicated by the white arrow).Acquiring the 2D images in the plane of the desired 3D reconstruction,is believed to optimize the resolution of the 3D images. As such, it ispreferred that the user define the plane of acquisition to be the sameas the plane of desired 3D reconstruction.

The number of series will also depend on the desired overlap. In orderto yield a 50% overlap, four separate series should be acquired andoverlapped into a composite data set (see FIGS. 3(a)-(d)). The compositedata set will have 4 times the number of images as each individualseries, but because the images are overlapped by 50%, the data setappears re-segmented into a smaller slice thickness also defined by n/k.Starting with a 4 mm slice thickness, a 50% overlap will result in thecomposite data file having an effective slice thickness of 2 mm. Recentexperiments suggest that the slice thickness remains at 4 mm but thedistance between slices in the composite series is shortened such thatthe slices appear “thinner” to the naked eye. In order to yield a 100%overlap, eight separate series should be acquired and overlapped (seeFIGS. 4(a)-(h)). Starting from a 4 mm slice thickness, a 100% overlapwill result in the composite data file having an effective slicethickness of 1 mm. Again, a 4 mm slice thickness facilitates thesemathematical calculations, but as noted, other slice thickness valuescan be used.

At step 204, the scanner 102 thereafter acquires the next image dataseries starting from the adjusted starting table position. FIG. 3(b)illustrates the result of step 204 when a 50% overlap is desired, with 4image slices being acquired for series 2 (the currently acquired seriesbeing indicated in boldface) starting from TP(0.5 n mm).

The flow of FIG. 2 will return to steps 202 and 204 depending upon theuser's desired amount of overlap (step 206). With a desired 50% overlap,the flow of FIG. 2 will return twice more to steps 202 and 204 to yieldthe results shown in FIGS. 3(c) and 3(d). With a desired 100% overlap,the flow of FIG. 2 will return to steps 202 and 204 six more times toyield the results shown in FIGS. 4(c)-(h).

Once all image data series are acquired, at step 208, the controlcomputer 104 preferably compiles all of the acquired slices for theplurality of series into a composite data file. This requiresinstructions to the control computer 104 as set forth in connection withFIGS. 8(g)-(k).

Experiments show that in order to assure proper interleaving the newcomposite series must be resorted (step 210). The scanner by defaultwill be set to sort by “instance” which means images are sortedsequentially and anatomically. In order for RIOT to work, images need tobe sorted by slice position. To do this manually, “Browser” is selectedunder the Patient file. “Local Database” is highlighted. The patientname and the composite sequence are then highlighted. “Slice position”is selected under the Sort file, as shown in FIG. 8(k). To ensure thatthe file remains correctly sorted, it is recommended that it be saved asdescribed above under a new name. Experimentation indicates thatalthough conventional sorting features available on computers 104 forscanner 102 can properly interleave slices by table position, themultiple steps involved may result in error. Should it be necessary,software modifications for scanner computer 104 to properly sort andinterleave slices by table position are readily within the skill of aperson having ordinary skill in the art following the teachings hereinas explained below in connection with FIG. 9. Once the slices within thecomposite data file are preferably stored by slice position/tableposition (step 210), the computer 104 will interleave and overlap theseries such that not only are the gaps of data filled, but the data isre-segmented giving the appearance of thinner slices. The slicethickness of the composite data set depends on the original slicethickness as well as # of series interleaved and is also defined as n/k.As noted above, the thickness of a composite series of four, eachobtained at 4 mm slice thickness will be 2 mm (or 50% of the originalslice thickness). In a 100% overlap instance, the composite series willbe re-segmented into 25% of the original 4 mm slice thickness, or 1 m(see FIG. 4(h)). Therefore, the slice thickness goes from n mm to n/kmm. One hundred percent overlap is currently not believed to be possibleeven with multi-detector CT technology. As mentioned above, in reality,a 50% overlap will be more than enough for most 3D reconstructions.

Mathematical analysis of the composite data set shows that there-segmented thinner slices are the result of approximations. Themathematical appendix appended hereto illustrates the mathematicalvalidity of this re-segmenting process. To assure exact segmentation,software modifications for scanner computer 104 will be necessary. Thisis readily within the skill of a person having ordinary skill in theart.

Thereafter, the composite data file can be exported to 3D graphicsrendering software (not shown) for display of MPRs and/or 3Dreconstructions. An example of a suitable 3D rendering software packageis the Voxar Site-wide 3D™ package produced by Voxar Limited ofEdinburgh, Scotland. It is believed that software modifications may benecessary with some 3D rendering programs to ensure that the integrityof the composite data file is maintained upon loading. Such softwaremodifications are believed to be well within the skill of a personhaving ordinary skill in the art.

It is worth noting that steps 200-210 of FIG. 2 can be performed withmanual intervention by the clinician between each series acquisition asexplained above to define the parameters for the next seriesacquisition. Alternatively, steps 200-210 can be performed automaticallyby the scanner control computer 104 without human intervention afterinvocation by a clinician of the start of the process. For example,steps 200-210 can be implemented in a software program executed bycontrol computer 104 after invocation of a “macro” function or the likeby the clinician through a control computer user interface. Thissoftware program could be configured to emulate the user input describedin connection with FIGS. 8(a)-(k).

To invoke such an automated function, the clinician may be prompted toprovide starting acquisition parameters such as image data series slicethickness, skip, the desired degree of overlap, starting table position,the plane of acquisition, etc. Once the staring parameters arespecified, the software can automatically compute table positionadjustments for subsequently acquired image series and automaticallycommunicate control instructions to the scanner for acquiring each imageseries.

Alternatively, rather than requiring a user to provide initialacquisition parameters, a plurality of appropriately named predefinedmacro functions, each with its own predefined acquisition parameters,can be made available for invocation by the clinician from the controlcomputer user interface.

The software program can also be configured to automatically perform thetask of sorting the image slices in the composite data set into order byslice position. FIG. 9 depicts a flowchart for this sorting algorithm.The sorting operation preferably begins when all of the image slices foreach of the acquired image series are obtained (step 900). Whenprogrammed on a conventional MRI scanner, the RIOT technique produces Toutput image series (usually 3≦T≦5, but this need not be the case) eachcontaining M images. Next, the software processes the header informationfor these slices to identify the slice position for each image slice(step 902). To produce the RIOT effect, the image slices in these seriesare preferably sorted in ascending order by their identified slicepositions (step 904), relabeled as a single RIOT image series of lengthT*M (step 906) and stored in memory as a single image series (step 908).

Virtually all contemporary medical image data acquisition systems formatimages use the Digital Image Communications in Medicine (DICOM)standard. DICOM image objects are formatted according to this well-knownstandard to include a header elements and image picture elements(pixels). For the purposes of explaining the preferred sorting software,the header consists of a set of known descriptive elements or attributesthat are encoded according to the standard as a tag-value pair. Bycorrectly interpreting and processing the DICOM headers of the imageslices, it is possible to adjust the image description attributescontained in each image slice to achieve the desired RIOT result.

As explained above, the sorting algorithm of FIG. 9 operates to read theheader of each DICOM image comprising the T original output series,extracts the relevant attributes by searching for the relevant DICOMtags, determines the correct sorted order and then updates the relevantDICOM tags so that a DICOM compliant display device which might receivethe resulting RIOT image series would display and process the images inthe most advantageous manner.

In order to accomplish the necessary RIOT sorting and series relabeling,the following DICOM image attributes may be used:

TABLE 1 DICOM Standard Data Elements which may be used to implement aRIOT Sorting Algorithm DICOM Attribute Data Element Tag DescriptionUsage 0020000e REL Series Modified to Instance UID indicate new RIOTseries 00200011 REL Series Number Renumbered to a single RIOT series00200013 REL Image Number Renumbered to indicate proper sort order00200032 REL Image Position Used to determine Patient relative sliceposition 00200037 REL Image Used to determine Orientation relative slice(Patient) position 00201041 REL Slice Location Used to determinerelative slice position 00080008 ID Image Type Modified to indicate theimage has been edited 00080018 ID SOP Instance Modified to UID indicatethe image has been edited

When any original DICOM image is edited or modified the DICOM standarddefines a set of attributes that must be modified in order to indicateand track the changes. For these attributes that are not listed in Table1 above, a person having ordinary skill in the art can make theappropriate modifications according to known DICOM conventions.

A possible embodiment of the sorting software described in connectionwith FIG. 9 would include a DICOM Storage Service Class Provider (SCP)module which would be addressable over a computer network by any imagingmodality (scanner) that supports the DICOM communication protocol as anappropriate Storage Service Class User (SCU). This DICOM SCP softwaremodule would receive unsorted image series from the imaging modality andtemporarily store them in a directory on a local storage device. Thesorting software module would be invoked using standard programmingmeans by the SCP module. The sorting module would read each DICOM formatimage, parse the header tags, locate and extract the relevantattributes, and store them in a table in computer memory along with thename of the file used to temporarily store the image on the localstorage device. Once this table was sorted in ascending order of slicelocation, the relevant image header tags will be redefined so that imageslices are numbered sequentially by slice location, and the number ofimage series is set to 1. These modified image attributes are thenwritten into the appropriate image file overwriting the previous values.Once the images are thus modified a final software module, a DICOMStorage SCU, is invoked to transfer the images to the desireddestination (normally a permanent storage device or image displayworkstation) via the DICOM communication protocol.

In terms of time, it is believed that, with RIOT, each individual seriescan be acquired more rapidly than a single series acquired with 0% skipby conventional techniques. This speed is the result of utilizing a 100%skip instead of the conventional 0-20% skip. The total acquisition timefor 4 series (regardless of the sequence) is believed to be less thanthat of any high resolution volumetric sequence. The total acquisitiontime of 8 series will be similar to that of a high resolution volumetricsequence (for most sequences). However, experimentation indicates thatthe total acquisition times are dramatically reduced using an ultra fastsequence such as True-FISP. In fact, the True-FISP sequence is thepreferred sequence for most reconstructions as it displays highresolution despite its fast acquisition time.

FIGS. 5(a)-(d) depict a 3D reconstruction derived from phantom imagesutilizing a True-FISP sequence for a (1) conventional non-RIOT sequencewith 4 mm slice thickness and a 0% skip (FIG. 5(a)), (2) two interleavedRIOT series, with slices having a 4 mm slice thickness and a 100% skip(FIG. 5(b)); (3) four interleaved RIOT series, with slices having a 4 mmslice thickness and a 100% skip (FIG. 5(c)); and (4) eight interleavedRIOT series, with slices having a 4 mm slice thickness and a 100% skip(FIG. 5(d)). FIG. 7(a) depicts a 3D reconstruction for True-FISP RIOTdata acquired with a 100% overlap. FIG. 7(b) depicts a coronal MPR forTrue-FISP RIOT data acquired with a 100% overlap.

With True-FISP, the total time required to acquire 8 series through anadult knee is 3 minutes. A high resolution volumetric sequence such as3-D Vibe (see FIG. 6 for a 3D-Vibe sequence of a phantom) will takeapproximately 15 minutes. Depending on the pathology, a longer sequence,such as a T1-weighted sequence, may be indicated for better definition.However, as stated earlier, RIOT may be applied to any 2D sequencetechnique.

Although RIOT was conceived for the purpose of improving 3-D andmulti-planar reconstructions, its impact on cross-sectional imaging isalso believed to be profound. The reason for this is that because eachindividual series is acquired with a 100% gap between slices,“cross-talk” (which results in decreased SNR with thinner slices inconventional sequences), is no longer an issue. Using conventionalnon-RIOT techniques, slice thicknesses less than 2.5 mm will exhibitpoor SNR, at times rendering images non-diagnostic. RIOT allows for eventhinner slice reconfigurations (i.e., 1 mm). In addition, some sequenceswill not allow slice thicknesses below a certain value due to SARlimitations. With RIOT, thinner slices will not result in increased SAR,regardless of the sequence. This is due to the fact that the thinnerslices are not acquired contiguously, (i.e. in one series). The resultis improved resolution without loss of SNR (unlike with conventional 2-Dand volumetric sequences). As noted, with RIOT, 4 series, each acquiredat 4 mm, slices can be reconfigured into a single series segmented at 2mm thick slices with a 50% overlap. Likewise, 8 such series can also bereconfigured into a single series segmented into 1 mm thick slices with100% overlap, without sacrificing SNR. The thinner slices of thecomposite data sets will have identical SNR as the original data sets.Therefore, RIOT allows for thinner slices with both excellent resolutionand excellent SNR. This will yield 3D reconstructions that surpass thoseof high resolution volumetric sequences (see FIGS. 5(c), (d), 6, 7(a)).Experimentation also indicates that MPR's and 3D images generated fromjust two interleaved series, each acquired with 4 mm thick slices and100% gap (which in effect yields a series of 4 mm thick slices, with 0skip and zero overlap), exhibit better resolution and SNR than thosegenerated using the same sequence with the same slice thickness and 0skip (compare FIGS. 5(a) and (b)). Yet, the acquisition time for bothtechniques is identical. Therefore, it is believed that RIOT will alsoimprove the diagnostic quality of cross-sectional images, which isespecially important when focusing on detailed structures.

It is worth noting that although RIOT is believed to enhance theinherent capability of any 2D sequence in terms of resolution and SNR,it is not believe that RIOT will overcome limitations inherent to aparticular sequence (such as chemical shift artifact).

The present invention is also believed to be suitable for use with CTscanners. CT experimentation for RIOT was carried out with scansperformed on a Siemens Sensations 16 row detector CT scannermanufactured by Siemens Medical Systems of Erlangen, Germany. Two CTdata sets of 2×1 mm were generated from a CT data set acquired with 1.5mm collimation at 5 mm slice thickness. The starting position of thesecond data set was 1 mm below the first. Both series were combined intoa composite data set sorted by TP to yield a single data set of 1×1 mmwith 100% overlap. This preliminary data suggests that interleaving maybe possible with CT. It is believed that the application of RIOT to CTwill result in even higher quality imaging than is currently possiblewith existing multi-detector technology.

In summary, RIOT allows for much higher quality 2D as well as MPR and 3Dimages with relatively little time cost. More importantly, it can beapplied to most MR sequences (all except volumetric acquisitions).Accordingly, it is believed that RIOT can improve MR imaging overallwhether 2D or 3D image sets. Finally, and most importantly, it isbelieved that RIOT will increase diagnostic accuracy, particularly whendealing with small anatomy which could particularly advantageouslyimpact pediatric MR imaging.

While the present invention has been described above in relation to itspreferred embodiment, various modifications may be made thereto thatstill fall within the invention's scope, as would be recognized by thoseof ordinary skill in the art. For example, in the preferred embodimentwhere 4 mm slices were acquired via RIOT to achieve a 50% overlap,series 1 was acquired beginning at a initial table position of TP(0 mm),series 2 was acquired beginning at a initial table position of TP(2 mm),series 3 was acquired beginning at a initial table position of TP(4 mm),and series 4 was acquired beginning at a initial table position of TP(6mm). However, the nature of initial table position adjustments need notnecessarily be sequential. For example, series 2 can be acquiredbeginning at a initial table position of TP(4 mm), series 3 can beacquired beginning at a initial table position of TP(6 mm), and series 4can be acquired beginning at a initial table position of TP(2 mm).Moreover, the examples given herein are described in terms of a 50%overlap (4 image series) or a 100% overlap (8 image series), but itshould be noted that other overlap percentages (e.g., 200%) could alsobe used in the practice or RIOT. These and other modifications to theinvention will be recognizable upon review of the teachings herein. Assuch, the full scope of the present invention is to be defined solely bythe appended claims and their legal equivalents.

Mathematical Appendix:

Sampling theory is used to increase the slice resolution in imaging forthe two most common methods of three-dimensional imaging—multi-slice 2Dand 3D imaging. In this discussion, a methodology is outlined for usingmulti-slice 2D imaging to increase the spatial resolution without thecorresponding decrease in measured signal.

Drawing on the mathematical framework in Haacke et al referenced above,the 2D image projected along z isp(x,y)=∫p(x,y,z)dzAs shown in FIG. 10(a), the voxel value is the area under the curve ofp(z) where δ is the thickness of the area to be examined under constantx and y

p(x₀, y₀) = ∫₀^(δ)p(x₀, y₀, z)𝕕zwhere p(z) represents the distribution of the protons and it is assumedthat p(z) is a well behaved function in the domain {0, δ} which we canpartition (as shown in FIG. 10(b)) and then write the equation as

S_(i) = ∫₀^(δ_(i))p(z)𝕕z = ∫₀^(1Δ)p(z)𝕕z + ∫_(1Δ)^(2Δ)p(z)𝕕z + ∫_(2Δ)^(3Δ)p(z)𝕕z + ∫_(3Δ)^(4Δ)p(z)𝕕z = a₀¹ + a₁² + a₂³ + a₃⁴This property can be used to overcome SAR and the slice thickness issueof SNR and resolution. To do this, a slice sequence is defined for amulti-slice measurement as follows (1, 3, 5 . . . ) and after theselected area of examination is completed, the region is re-scanned witha slice shift as shown below. Lets define p_(n) for the voxel value atthe n^(th) slice and a_(i) ^(j) for the area under given by

∫_(i Δ)^(jA)p(z)𝕕zas the voxel value for a slice of thickness Δ at position iΔ to jΔ. InFIG. 11, each segment is of length Δ and the thickness of each sliceS_(i) will vary from 4-6 Δ and have an offset from nΔ from the initialstart position. To better illustrate the slices, they are depicted inFIG. 11 in different grayscale colors and slightly raised to show theirrelative positions.

The following linear system of equations can thus be generated:

S₀ = ∫₀^(1Δ)p(z)𝕕z + ∫_(1Δ)^(2Δ)p(z)𝕕z + ∫_(2Δ)^(3Δ)p(z)𝕕z + ∫_(3Δ)^(4Δ)p(z)𝕕z = a₀¹ + a₁² + a₂³ + a₃⁴S₁ = ∫_(1Δ)^(2Δ)p(z)𝕕z + ∫_(2Δ)^(3Δ)p(z)𝕕z + ∫_(3Δ)^(4Δ)p(z)𝕕z + ∫_(4Δ)^(5Δ)p(z)𝕕z = a₁² + a₂³ + a₃⁴ + a₄⁵S₂ = ∫_(2Δ)^(3Δ)p(z)𝕕z + ∫_(3Δ)^(4Δ)p(z)𝕕z + ∫_(4Δ)^(5Δ)p(z)𝕕z + ∫_(5Δ)^(6Δ)p(z)𝕕z = a₂³ + a₃⁴ + a₄⁵ + a₅⁶S₃ = ∫_(3Δ)^(4Δ)p(z)𝕕z + ∫_(4Δ)^(5Δ)p(z)𝕕z + ∫_(5Δ)^(6Δ)p(z)𝕕z + ∫_(6Δ)^(7Δ)p(z)𝕕z = a₃⁴ + a₄⁵ + a₅⁶ + a₆⁷S₄ = ∫_(4Δ)^(5Δ)p(z)𝕕z + ∫_(5Δ)^(6Δ)p(z)𝕕z + ∫_(6Δ)^(7Δ)p(z)𝕕z + ∫_(7Δ)^(8Δ)p(z)𝕕z = a₄⁵ + a₅⁶ + a₆⁷ + a₇⁸S₅ = ∫_(2Δ)^(3Δ)p(z)𝕕z + ∫_(3Δ)^(4Δ)p(z)𝕕z + ∫_(4Δ)^(5Δ)p(z)𝕕z + ∫_(5Δ)^(6Δ)p(z)𝕕z + ∫_(6Δ)^(7Δ)p(z)𝕕z = a₂³ + a₃⁴ + a₄⁵ + a₅⁶ + a₆⁷S₆ = ∫_(1Δ)^(2Δ)p(z)𝕕z + ∫_(2Δ)^(3Δ)p(z)𝕕z + ∫_(3Δ)^(4Δ)p(z)𝕕z + ∫_(4Δ)^(5Δ)p(z)𝕕z + ∫_(5Δ)^(6Δ)p(z)𝕕z = a₁² + a₂³ + a₃⁴ + a₄⁵ + a₅⁶S₇ = ∫₀^(1Δ)p(z)𝕕z + ∫_(1Δ)^(2Δ)p(z)𝕕z + ∫_(2Δ)^(3Δ)p(z)𝕕z + ∫_(3Δ)^(4Δ)p(z)𝕕z + ∫_(4Δ)^(5Δ)p(z)𝕕z = a₀¹ + a₁² + a₂³ + a₃⁴ + a₄⁵that can be solved as follows:

${\begin{bmatrix}11110000 \\01111000 \\00111100 \\00011110 \\00001111 \\00111110 \\01111100 \\11111000\end{bmatrix} \times \begin{bmatrix}a_{0}^{1} \\a_{1}^{2} \\a_{2}^{3} \\a_{3}^{4} \\a_{4}^{5} \\a_{5}^{6} \\a_{6}^{7} \\a_{7}^{8}\end{bmatrix}} = \begin{bmatrix}S_{0} \\S_{1} \\S_{2} \\S_{3} \\S_{4} \\S_{5} \\S_{6} \\S_{7}\end{bmatrix}$which can be solved if the determinant of the matrix is not equal tozero. This operation will need to be done for each voxel and the numberof slices and the slice shift needs to be selected such that the linearsystem of equations generated can be solved for the matrix of a_(i)^(j). It should be noted that solution is not unique and variouscombinations of slice thickness and slice positions will yield results.This analytical solution is computationally intensive and variousapproximate solutions can be used to solve for the matrix of a_(i) ^(j).

What is claimed is:
 1. A method of acquiring medical image data of apatient's region of interest, the method comprising: acquiring, by amedical imaging scanner, a first two-dimensional (2D) image series of apatient's region of interest, the first 2D image series comprising aplurality of 2D image slices, the image slices of the first 2D imageseries starting from an initial position in a coordinate system andhaving a predetermined skip therebetween, each image slice of the first2D image series having a predetermined slice thickness; and repeatingthe acquiring step to separately acquire one or more additional 2D imageseries of the patient's region of interest until a desired degree ofoverlap between the separately acquired first and additional 2D imageseries is achieved, wherein each separate additional 2D image series isacquired using the same image slice thickness and the same skip betweenimage slices as the first 2D images series but with an adjusted initialposition in the coordinate system such that a plurality of theseparately acquired 2D image series start from initial positions in thecoordinate system that are less than the predetermined slice thicknessfrom each other to thereby define overlaps between the separatelyacquired first and additional 2D image series.
 2. The method of claim 1wherein the repeating step comprises: adjusting the initial position foruse in a first of the separately acquired additional 2D image seriessuch that the difference between the adjusted initial position for thefirst of the separately acquired additional 2D image series and theinitial position for the first 2D image series is less than thepredetermined slice thickness; and acquiring, by the medical imagingscanner, a plurality of 2D image slices of the patient's region ofinterest as the first of the separately acquired 2D image series, theimage slices of the first of the separately acquired 2D image seriesstarting from the adjusted initial position and having the predeterminedskip therebetween, each image slice of the first of the separatelyacquired 2D image series having the predetermined slice thickness. 3.The method of claim 1 whereby the image slices of the same 2D imageseries are non-overlapping with respect to each other.
 4. The method ofclaim 3 wherein the desired degree of overlap provides an effectiveslice thickness of 2 mm.
 5. The method of claim 3 further comprising: aprocessor generating a three-dimensional (3D) volume of the region ofinterest based on the image slices of a plurality of the separatelyacquired 2D image series.
 6. The method of claim 5 wherein the medicalimage data is computed tomography (CT) image data acquired by a CTscanner.
 7. The method of claim 5 wherein the separately acquired 2Dimage series are acquired along any one selected from the groupconsisting of an axial plane, a sagittal plane, and a coronal plane. 8.The method of claim 5 wherein the medical image data is magneticresonance (MR) image data acquired by a MR scanner, wherein thecoordinate system position is a table position for the MR scanner. 9.The method of claim 8 further comprising a processor aggregating theplurality of image slices from the plurality of separately acquired 2Dimage series into a composite data file.
 10. The method of claim 9further comprising the processor sorting the image slices in thecomposite data file by the table position at which each image slice wasacquired.
 11. The method of claim 10 further comprising generating agraphical display of a three-dimensional reconstruction of the patient'sregion of interest from the image slices within the composite data file.12. The method of claim 10 further comprising generating a graphicaldisplay of a multi-planar reconstruction of the patient's region ofinterest from the image slices within the composite data file.
 13. Themethod of claim 10 further comprising segmenting the sorted image slicesin the composite data file to effectuate a thinner slice thickness forthe image slices in the composite data file.
 14. The method of claim 10wherein the desired degree of overlap is 50%.
 15. The method of claim 14wherein the predetermined slice thickness is n mm, and wherein theadjusted initial table position between each separately acquired 2Dimage data series is an adjustment of n/2 mm.
 16. The method of claim 15wherein n is 4 mm.
 17. The method of claim 15 wherein the predeterminedskip between image slices is 100% of the predetermined slice thickness.18. The method of claim 10 wherein the desired degree of overlap is100%.
 19. The method of claim 18 wherein the predetermined slicethickness is n mm, and wherein the adjusted initial table positionbetween each separately acquired 2D image series is an adjustment of n/4mm.
 20. The method of claim 5 wherein the separately acquired 2D imageseries are acquired along a user-selected plane of any obliquity. 21.The method of claim 20 wherein the separately acquired 2D image seriesare acquired in a plane that is the same as a desired plane of 3Dreconstruction.
 22. A computer program product for instructing a medicalimaging scanner to acquire medical image data of a patient's region ofinterest (ROI), the computer program product comprising: a plurality ofinstructions executable by a processor and resident on a non-transitorycomputer readable storage medium, wherein the instructions areconfigured to (1) instruct the scanner to acquire an firsttwo-dimensional (2D) image series of a patient's ROI, the first 2D imageseries comprising a plurality of 2D image slices, the image slices ofthe first 2D image series starting from an initial position in acoordinate system and having a predetermined skip therebetween, eachimage slice of the first 2D image series having a predetermined slicethickness, and (2) further instruct the scanner to separately acquireone or more additional 2D image series of the patient's ROI until adesired degree of overlap between the separately acquired first andadditional 2D image series is achieved, wherein each separate additional2D image series is acquired using the same image slice thickness and thesame skip between image slices as the first 2D image series acquisitionbut with an adjusted initial position in the coordinate system such thata plurality of the separately acquired 2D image series start frominitial positions in the coordinate system that are less than thepredetermined slice thickness from each other to thereby define overlapsbetween the separately acquired first and additional 2D image series.23. The computer program product of claim 22 whereby the image slices ofthe same 2D image series are non-overlapping with respect to each other.24. The computer program product of claim 23 wherein the instructionsare further configured to generate a three-dimensional (3D) volume ofthe patient's ROI based on the image slices of a plurality of theseparately acquired 2D image series.
 25. An apparatus for instructing amedical imaging scanner to acquire medical image data of a patient'sregion of interest (ROI), the apparatus comprising: a processorconfigured to instruct the scanner to (1) acquire a firsttwo-dimensional (2D) image series of a patient's ROI, the first 2D imageseries comprising a plurality of 2D image slices, the image slices ofthe first 2D image series starting from an initial position in acoordinate system and having a predetermined skip therebetween, eachimage slice of the first 2D image series having a predetermined slicethickness, and (2) separately acquire one or more additional 2D imageseries of the patient's ROI until a desired degree of overlap betweenthe separately acquired first and additional 2D image series isachieved, wherein each separate additional 2D image series is acquiredusing the same image slice thickness and the same skip between imageslices as the first 2D image series acquisition but with an adjustedinitial position in the coordinate system such that a plurality of theseparately acquired 2D image series start from initial positions in thecoordinate system that are less than the predetermined slice thicknessfrom each other to thereby define overlaps between the separatelyacquired first and additional 2D image series.
 26. The apparatus ofclaim 25 wherein the processor comprises a control computer for thescanner.
 27. The apparatus of claim 25 whereby the image slices of thesame 2D image series are non-overlapping with respect to each other. 28.The apparatus of claim 27 wherein the processor is further configured togenerate a three-dimensional (3D) volume of the patient's ROI based onthe image slices of a plurality of the separately acquired 2D imageseries.